Learning sparse relational transition models
Abstract
We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.
Cite
@article{arxiv.1810.11177,
title = {Learning sparse relational transition models},
author = {Victoria Xia and Zi Wang and Leslie Pack Kaelbling},
journal= {arXiv preprint arXiv:1810.11177},
year = {2018}
}